Senior Machine Learning Engineer

On-site | Palo Alto, California

Confidential search via external partner

Overview

I am representing a well-funded, early-stage Silicon Valley startup building a core AI platform from first principles. This is a zero-to-one role with real technical ownership, working closely with experienced founders and a small senior engineering team.

This is a hands-on engineering role. Not research-only. Not people management. You will design, build, and ship production ML systems that power real customer-facing products.

What this role actually is

You own ML systems end to end, from problem framing to production impact

You make architectural decisions, not just model choices

You decide when ML is the right solution and when it is not

You build systems that are fast, reliable, observable, and cost-efficient

You operate close to product, code, and infrastructure

This role suits engineers who enjoy ambiguity, responsibility, and building foundational systems rather than iterating on legacy stacks.

Responsibilities

Design, build, and deploy production-grade ML systems solving real-world problems
Develop models for high-quality parsing and extraction across structured and semi-structured data, including HTML
Select the right technical approach per problem, including fine-tuning, RAG, reinforcement learning, hybrid systems, or deterministic solutions
Fine-tune and RL-train models as needed, iterating based on live performance
Optimise systems for low latency and low cost without sacrificing quality
Own the full ML lifecycle: data collection, feature engineering, training, evaluation, deployment, monitoring, and iteration
Build scalable ML pipelines and low-latency inference systems suitable for production
Collaborate closely with product and platform engineers to integrate ML into customer-facing applications
Define best practices around experimentation, model versioning, evaluation, and monitoring

Requirements

10+ years of experience in machine learning, applied AI, or related engineering roles
Strong fundamentals in machine learning, statistics, and algorithms
Demonstrated experience deploying ML systems into production environments
Deep hands-on experience with Python and ML frameworks such as PyTorch, TensorFlow, or scikit-learn
Experience with LLMs, embeddings, fine-tuning, RAG pipelines, and reinforcement learning
Experience optimising production systems for performance, latency, and cost
Familiarity with modern ML infrastructure and data stacks, including cloud platforms, feature stores, vector databases, and orchestration tools
Ability to operate effectively in fast-moving, ambiguous startup environments

Nice to have

Experience building parsing, extraction, or transformation systems
Background in personalization, search, recommender systems, or NLP
Early-stage startup experience or building systems from scratch
Prior mentorship or technical leadership experience
Experience working with privacy-sensitive or regulated data

Why strong candidates engage with this role

True zero-to-one ML ownership

Direct access to founders and architectural influence

Real-world ML problems, not demos or research theatre

High accountability and high trust environment

Competitive compensation, meaningful equity, long-term upside

If interested, reply with your resume.

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